Popularity biases are known to arise from the underlying characteristics of the data, such as the number of interactions per item or user, sparsity, and an unbalanced distribution of interactions among items. As a result, in this work, we aim to reproduce the research study of Abdollahpouri et al. [1]} and conduct it in a different domain and dataset. In particular, we select the book domain and Book-Crossing dataset due to a variety of reasons. First, the Book-Crossing data characteristic differs significantly from the previous study on the MovieLens 1M dataset in that it reports (\(256.46\) , \(165.59\) ) interactions per item and users respectively, as opposed to (\(12.79\) , \(13.92\) ) in Book-Crossing dataset. Table REF  summarizes the main data characteristic of Book-Crossing dataset.
Additionally, there are several aspects of book recommendations that make them different, challenging, and somehow more important than recommendations entertainment industry, such as movie and music recommendations [2]}, [3]}. For instance, although book-reading must become more prevalent in society, the investigation proves the opposite i.e., the practice of reading for pleasure or education is declining, particularly among the young [2]}. Also, since a reader spends much time reading books, it is crucial that the content matches their known preferences and expectations. The experience of finding a match that is suitable can be rewarding for readers, but an inappropriate recommendation could result in losing interest, further contributing to the downward trend. It is, therefore, crucial to examine the existence of popularity bias from a user-centered perspective in book recommendations and, for reasons of comparability, we would like to answer the same two research questions as in the study of Abdollahpouri et al. [1]}:
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